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Patrick Flaherty

Assistant Professor

My lab develops statistical algorithms to interpret and integrate data generated by next-generating sequencing and other high-throughput experimental platforms to improve the diagnosis and treatment of genetic diseases. Towards this aim, my lab is pursuing two main research directions: (1) detecting rare single nucleotide variants with next generation sequencing data from heterogeneous cell populations, and (2) estimating genomic subtypes with complex data types from clinical cancer samples. These aims each have the dual purposes of advancing statistical methodologies for analyzing massive, complex data sets, and advancing our understanding of fundamental biological processes that lead to the development and progression of disease. Achievement of the aims of this research will broadly mean better targeted therapeutic strategies for many types of cancer, improved diagnostics for viral and other infectious diseases, and improved methods for statistical inference in large genomic data sets.

Current Research
Detecting rare single nucleotide mutations from next-generation sequencing data is important for identifying resistance in virus populations, cell-free DNA diagnostics, and longitudinal chemotherapy treatment monitoring. However, there is a lack of accurate, scalable statistical methods to identify variant alleles from massive next-generation sequencing data sets. Previously, we developed a hierarchical Bayesian statistical model and statistical inference algorithm to identify rare variants in next-generation sequencing data. Now, we are extending that model and using it to study all mutations that modulate antibiotic resistance in target proteins.

An important and unresolved question in the study of the development of solid cancerous tumors is how cells with distinct genomic subtypes within the same tumor cooperate or interfere and lead to growth or resistance to therapy. A better understanding of this phenomena will allow physicians to better assess the content of the entire heterogeneous tumor and design combination therapies that target multiple subtypes simultaneously. We previously developed, a mixed membership statistical model that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample. The next steps toward understanding heterogeneous tumor development is to improve our model to integrate diverse genomic assay data and characterize the co-occurrence of genomic subtypes in primary cancer samples using massive, distributed, genomic data sets.

Learn more at www.math.umass.edu/directory/faculty/patrick-flaherty

Academic Background

  • BS Electrical Engineering, Rochester Institute of Technology
  • PhD Electrical Engineering & Computer Science, UC Berkeley
  • Postdoc Biochemistry, Stanford University
He Y, Zhang F, Flaherty P. “RVD2: An ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data” Bioinformatics 2015 September 1. PMID: 25931517.
Flaherty P, Davis RW. “Robust Optimization of Biological Protocols” Technometrics 2015 Jul 13;57(2):234-244. PMID: 26417115.
Yan S, Tsurumi A, Que, Y, Ryan, CM, Bandyopadhaya A, Morgan AA, Flaherty PJ, Tompkins RG, Rahme LG. “Prediction of Multiple Infections After Severe Burn Trauma: a Prospective Cohort Study” Annals of Surgery 2014 Jun 19. PMID: 24950278.
Saddiki H, McAuliffe JM, Flaherty P. “GLAD: A mixed-membership model for heterogeneous tumor subtype classifcation” Bioinformatics 2014 Sep 29.
Flaherty P, Radhakrishnan M, Dinh, T, Jordan MI, Arkin AP. “A Dual Receptor Cross-talk Model of G protein-coupled Signal Transduction in RAW Macrophage Cells" PLOS Computational Biology 2008 September; 4(9):1-11.
Flaherty P, Giaever G, Kumm J, Jordan MI, Arkin AP. “A Latent Variable Model for Chemogenomic Profling" Bioinformatics, 2005 Aug 1; 21(15):3286-93.
Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B, Arkin AP, Astromo A, El-Bakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian KD, Flaherty P, Foury F, Garnkel DJ, Gerstein M, Gotte D, Guldener U, Hegemann JH, Hempel S, Herman Z, Jaramillo DF, Kelly DE, Kelly SL, Kotter P, LaBonte D, Lamb DC, Lan N, Liang H, Liao H, Liu L, Luo C, Lussier M, Mao R, Menard P, Ooi SL, Revuelta JL, Roberts CJ, Rose M, Ross-Macdonald P, Scherens B, Schimmack G, Shafer B, Shoemaker DD, Sookhai-Mahadeo S, Storms RK, Strathern JN, Valle G, Voet M, Volckaert G, Wang CY, Ward TR, Wilhelmy J, Winzeler EA, Yang Y, Yen G, Youngman E, Yu K, Bussey H, Boeke JD, Snyder M, Philippsen P, Davis RW, Johnston M. “Functional Profling of the Saccharomyces cerevisiae Genome" Nature 2002 Jul 25; 418(6896):387-91.
Giguere, C., Dubey, H.V., Sarsani, V.K. et al. SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data. BMC Bioinformatics 21, 215 (2020). https://doi.org/10.1186/s12859-020-03550-1
Zhang F., Wang C., Trapp A.C., Flaherty P. (2019) A Global Optimization Algorithm for Sparse Mixed Membership Matrix Factorization. In: Zhang L., Chen DG., Jiang H., Li G., Quan H. (eds) Contemporary Biostatistics with Biopharmaceutical Applications. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-15310-6_7
Contact Info

Department of Mathematics & Statistics
LGRT 1336
710 North Pleasant Street
Amherst, MA 01003-9292

(413) 545-7690